choicevar

This function handles choice-specific covariates in multinomial choice
models. See the example for an example of useage.

manip

Contains functions to perform Bayesian
inference using posterior simulation for a number of
statistical models. Most simulation is done in compiled C++
written in the Scythe Statistical Library Version 1.0.3. All
models return 'coda' mcmc objects that can then be summarized
using the 'coda' package. Some useful
utility functions such as density functions,
pseudo-random number generators for statistical
distributions, a general purpose Metropolis sampling algorithm,
and tools for visualization are provided.

Jong Hee Park

MCMCpack

Markov Chain Monte Carlo (MCMC) Package

Ghislain Vieilledent

Michael Malecki

Matthew Blackwell

Keith Poole

Craig Reed

Ben Goodrich

Qiushi Yu

Ross Ihaka

Pierre L'Ecuyer

Makoto Matsumoto

Takuji Nishimura

choicevar function

<dl><dt>var</dt>
<dd>The is the name of the variable in the dataframe.</dd>
<dt>varname</dt>
<dd>The name of the new variable to be created.</dd>
<dt>choicelevel</dt>
<dd>The level of <code>y</code> that the variable corresponds to.</dd></dl>

Arguments

Handle Choice-Specific Covariates in Multinomial Choice Models — choicevar

<dl>

<dt>var</dt>
<dd>The is the name of the variable in the dataframe.</dd>


<dt>varname</dt>
<dd>The name of the new variable to be created.</dd>


<dt>choicelevel</dt>
<dd>The level of <code>y</code> that the variable corresponds to.</dd>

</dl>

Handle Choice-Specific Covariates in Multinomial Choice Models

choicevar: Handle Choice-Specific Covariates in Multinomial Choice Models

Description

Usage

Value

Arguments

See Also